Falls are very common and extremely dangerous events for the elderly, constituting the leading cause of injury and incurring costs likely to exceed $68 Billion by 2020. Falls cause more than 95% of hip fractures and lateral falls (to the side) in particular contribute to 76% of all hip fractures. Most falls occur while walking and humans are inherently more unstable laterally when walking. Also, very limited scientific evidence exists to guide de- sign of interventions to improve walking function in the elderly. Thus, there is a clear need to identify the control strategies elderly use to maintain lateral balance while walking and to develop effective, evidence-based treatment strategies to improve lateral balance control in high fall risk elderly to reduce their risk of falls. The primary goal f this study is to develop interventions to help prevent falls. This requires intervening before fall occur. As people age, multiple physiological changes increase intrinsic physiological (neuromuscular) noise and decrease control authority (the ability to effectively regulate movements). Either or both of these can increase walking variability, which may contribute to falls. However, not all variability is detrimental. Increasing variability can sometimes even facilitate adaptability and improve recovery in locomotor rehabilitation. We recently developed novel computational control theory models that separate physiological noise from control authority to identify how walking humans exploit redundancy to regulate variability in the sagittal plane. We have now extended this work to determine how humans control lateral stepping movements in the frontal plane. Real-world walking tasks require humans both to be able to respond to changing task goals and also to choose effective strategies. Here, Aim 1 will determine how elderly with Low Fall Risk or High Fall Risk respond to externally imposed challenges (enforced step width and/or lateral perturbations). We will integrate our theoretical framework with computational models and experiments to differentiate effects of control from those of variability. Separately, Aim 2 will determine how elderly make (cognitive) internal choices to either avoid risk or fortify themselves against potential risk. We will again integrate experiments, models, and analyses to identify how Low and High Fall Risk elderly choose different risk-sensitive strategies. Aim 3 will determine if a targeted virtual reality based intervention that challenges people to both respond to imposed changes in lateral position, and also to choose effective strategies for doing so, can improve lateral stepping control and walking balance in High Fall Risk elderly. In a randomized, active control treatment trial, we will compare pre- and post-intervention changes in walking ability both with and without lateral perturbations, performance in a novel real-world-like navigation task, and established clinical assessments of walking and balance function. This study will apply novel experimental and rigorous computational and analytical approaches to greatly improve our understanding of how elderly individuals walk. We will translate this knowledge into clinical practice by implementing novel VR-based interventions that promise to improve walking function in high fall risk elderly.